Integrated authoring and translation system

ABSTRACT

The present invention is a system of integrated, computer-based processes for monolingual information development and multilingual translation. An interactive text editor enforces lexical and grammatical constraints on a natural language subset used by the authors to create their text, which they help disambiguate to ensure translatability. The resulting translatable source language text undergoes machine translation into any one of a set of target languages, without the translated text requiring any postediting.

This is a continuation application of application Ser. No. 08/363,309,filed Dec. 22, 1994.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to computer-based documentcreation and translation system and, more particularly, to a system forauthoring and translating constrained-language text to a foreignlanguage with no pre- or post-editing required.

2. Related Art

Every organization whose activities require the generation of vastquantities of information in a variety of documents is confronted withthe need to ensure their full intelligibility. Ideally, such documentsshould be authored in simple, direct language featuring all necessaryexpressive attributes to optimize communication. This language should beconsistent so that the organization is identified through its single,stable voice. This language should be unambiguous.

The pursuit of this kind of writing excellence has led to theimplementation of various disciplines designed to bring the authoringprocess under control. Yet authors of varied capabilities andbackgrounds cannot comfortably be made to fit a uniform skill standard.Writing guidelines, rules and standards are elusive--difficult to defineand enforce. Efforts aimed at both standardizing and improving on thequality of writing tend to meet with mixed results. However achieved andhowever successful, these results push up documentation authoring costs.

Recent attempts at surrounding authors with the software environmentthat might enhance their productivity and the quality of their writinghave only succeeded in providing spell checkers. The effectiveness ofother writing software has so far been disappointingly weak.

When the need to deliver information calls for the crossing oflinguistic frontiers, the challenges multiply. The organization thatneeds to clear a channel for its information flow finds itself to agreat extent, if not totally, dependent on translation.

Translation of text from one language to another language has been donefor hundreds of years. Prior to the advent of computers, suchtranslation was done completely manually by experts, called translators,who were fluent in the language of the original text (source text) andin the language of the translated text (target text). Typically, it waspreferable for the translator to have originally learned the targetlanguage as his/her native tongue and subsequently have learned thesource language. Such an approach was felt to result in the mostaccurate and efficient translation.

Even the most expert translator must take a considerable amount of timeto translate a page of text. For example, it is estimated that an experttranslator translating technical text from English to Japanese can onlytranslate approximately 300 words (approximately one page) per hour. Itcan thus be seen that the amount of time and effort required totranslate a document, particularly a technical one, is extensive.

The requirements for translation in business and commerce has grownsteadily in the last hundred years. This is due to several factors. Oneis the rapid increase in the text associated with conducting businessinternationally. Another is the large number of languages that suchtexts must be translated into in order for a company to engage in globalcommerce. A third is the rapid pace of commerce which has resulted infrequent revisions of text documents, which requires subsequenttranslation of new versions.

Many organizations have the responsibility for creating and distributinginformation in multiple languages. In the global marketplace, themanufacture must ensure that the manuals are widely available in thehost languages of their target markets. Manual translation of documentsinto foreign languages is a costly, time-consuming, and inefficientprocess. Translations are usually inconsistent owing to the individualinterpretation of the translators who are not necessarily well-versed inthe application specific language used in the documentation. Because ofthese problems, fewer manuals than would be ideal are actuallytranslated.

In the areas of research and development, the explosion of knowledgewhich has occurred in the last century has also geometrically increasedthe need for the translation of documents. No longer is there onepredominant language for documents in a particular field of research anddevelopment. Typically, such research and development activities aretaking place in several advanced industrialized countries, such as, forexample, the United States, United Kingdom, France, Germany, and Japan.Many times there are additional languages containing important documentsrelating to the particular area of research and development. Advances intechnology, particularly in electronics and computers, have furtheraccelerated the production of text in all languages.

The ability to produce text is directly proportional to the capabilityof the technology that is used. When documents had to be hand-written,for example, an author could only produce a certain number of words perunit of time. This increased significantly, however, with the advent ofmechanical devices, such as typewriters, mimeograph machines, andprinting presses. The advent of electronic, computer, and opticaltechnology increased the capability of the author even further. Today,an average author can produce significantly more text in a given unit oftime than any author could produce using the hand-written methods of thepast.

This rapid increase in the amount of text, coupled with enormousadvances in technology, has caused considerable attention to be paid tothe subject of translation of text from its source language to a targetlanguage(s). Considerable research has been done in universities as wellas in private and governmental laboratories, which has been devoted totrying to figure out how translation can be accomplished without theintervention of a human translator.

Computer-based systems have been devised which attempt to performmachine translation (MT). Such computer systems are programmed so as toattempt to automatically translate source text as an input into targettext as an output. However, researchers have discovered that suchcomputer systems for automatic machine translation are impossible toimplement using present technology and theoretical understanding. Nosystem exists today which can perform the machine translation of asource natural language to a target natural language without some typeof editing by expert editors/translators. One method is discussed below.

In a process called pre-editing, source text is initially reviewed by asource editor. The task of the source editor is to make changes to thesource text so as to bring it into conformance with what is known to bethe optimal state for translation by the machine translation system.This conformance is learned by the source editor through trial anderror.

The pre-editing process just described may go through iterations byadditional source editors of increasing competence. The source text thusprepared is submitted for processing to the machine translation system.The output is target language text which, depending on the purposes ofthe translation of quality requirements of the user, may or may not bepost-edited.

If the translation quality required must be comparable to that ofproficient human translation, the output of machine translation willmost likely have to be post-edited by a competent translator. This isdue to the complexity of human language and the comparatively modestcapabilities of the machine translation systems that can be built withpresent technology, within natural limitations of time and resources,and with a reasonable expectation of meeting cost-effectivenessrequirements. Most of the modest systems that are built require, indeed,the post-editing activity, intended to approximate, by whatever measure,the quality levels of purely human translation.

Once such system is the KBMT-89 designed by the Center for MachineTranslation, Carnegie Mellon University, which translates English toJapanese and Japanese to English. It operates with a knowledge baseddomain model which aids in interactive disambiguation (i.e., editing ofthe document to make it unambiguous). However, this interactivedisambiguation is not typically done interactively with an author. Oncethe system finds an ambiguous sentence that it cannot disambiguate, itmust stop the process and resolve ambiguities by asking aauthor/translator a series of multiple-choice questions. In addition,since the KBMT-89 does not utilize a well-defined controlled inputlanguage the so-called translator assisted interactive disambiguationproduces text which requires post-editing.

In view of the above, it would be advantageous to have a translationsystem that eliminates both pre- and post-editing.

SUMMARY OF THE INVENTION

The present invention is a system of integrated, computer-basedprocesses for monolingual document development and multilingualtranslation. An interactive computerized text editor enforces lexicaland grammatical constraints on a natural language subset used by theauthors to create their text, and supports the authors in disambiguatingtheir text to ensure its translatability. The resulting translatablesource language text undergoes machine translation into any one of a setof target languages, without the translated text requiring anypost-editing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1(a) and 1(b) are high level block diagrams of the architecture ofthe present invention.

FIG. 2 is a high level flowchart of the operation of the presentinvention.

FIG. 3 is a high level informational flow and architectural blockdiagram of MT 120.

FIG. 4 shows an example of an information element.

FIG. 5 is a block diagram of the domain model 500.

FIG. 6 is a high level flow diagram of the operation of the languageeditor 130.

FIG. 7 is a flow diagram illustrating the operation of the vocabularychecker 610.

FIG. 8 is a high level flow diagram of the disambiguation block 630.

FIG. 9 is an informational flow and architectural block diagram of MT120.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

I. Integrated System Overview

The computer-based system of the present invention provides functionalintegration of:

1) An authoring environment for the development of documents, and

2) A module for accurate, machine translation into multiple languageswithout pre- or post-editing.

Utilizing this technology in the production of multilingualdocumentation, the user is assured of consistently accurate, timely,cost-efficient translation, whether in small or large volumes, and withvirtually simultaneous release of information in both the sourcelanguage and the languages targeted for translation.

The decision to link the source language authoring function togetherwith the translation function is based on two principles:

1) In a multinational, multilingual business environment, theinformation is not considered to be fully developed until it isdeliverable in the various languages of the users.

2) Combining the authoring and translation processes within a unifiedframework leads to efficiency gains that cannot otherwise be achieved.

FIG. 1(a) shows a high level block diagram of the Integrated Authoringand Translation System (IATS) 105. The IATS 105 provides a specializedcomputing environment dedicated to supporting an organization inauthoring documentation in one language and translating it into variousothers. These two distinct functions are supported by an integratedgroup of programs, as follows:

1) Authoring--one subgroup of the programs provides an interactivecomputerized Text Editor (TE) 140 which enables authors to create theirmonolingual text within the lexical and grammatical constraints of adomain-bound subset of a natural language, the subset designatedConstrained Source Language (CSL). Additionally, the TE 140 enablesauthors to further prepare the text for translation by guiding themthrough the process of text disambiguation which renders the texttranslatable without pre-editing;

2) Translation--another subgroup of the programs provides the MachineTranslation (MT) 120 function, capable of translating the CSL into asmany target languages as the generator module has been programmed togenerate, with the resulting translation requiring no post-editing.

For a system that features translation as a central component, theintegration of the authoring and the translation functions of thepresent invention within a unified framework is the only way devised todate that eliminates both pre- and postediting.

The text (TE) 140 is a set of tools to support the authors and editorsin creating documents in CSL. These tools will help authors to use theappropriate CSL vocabulary and grammar to write their documents. The TE140 communicates with the author 160 (and vice versa) directly.Referring to FIG. 1(b), the IATS 105 is divided into four main parts toperform the authoring and translation functions: (1) a ConstrainedSource Language (CSL) 133, (2) a Text Editor (LE) 140, (3) a MT 120, and(4) a Domain Model (DM) 137. The Text Editor 140 includes a LanguageEditor 130 and a Graphics Editor 150. In addition, a File ManagementSystem (FMS) 110 is also provided for controlling all processes.

The CSL 133 is a subset of a source language whose grammar andvocabulary cover the domain of the author's documentation which is to betranslated. The CSL 133 is defined by specifications of the vocabularyand grammatical constructions allowed so that the translation process ismade possible without the aid of pre- and post editing.

The TE 140 is a set of tools to support authors and editors in creatingdocuments in CSL. These tools will help authors to use the appropriateCSL vocabulary and grammar to write their documents. The LE 130communicates with the author 160 (and vice versa) via the text editor140. The author has bi-directional communication via line 162 with thetext editor 140. The LE 130 informs the author 160 whether words andphrases that are used are in CSL. The LE 130 is able to suggest synonymsin CSL for words that are relevant to the domain of information whichincludes this document, but are not in CSL. In addition, the LE 130tells an author 160 whether or not a piece of text satisfies CSLgrammatical constraints. It also provides an author with support indisambiguating sentences that may be syntactically correct but aresemantically ambiguous.

The MT 120 is divided into two parts: a MT analyzer 127 and a MTgenerator 123. The MT analyzer 127 serves two purposes: it analyzes adocument to ensure that the document unambiguously conforms to CSL andproduces interlingua text. The analyzed CSL-approved text is thentranslated into a selected foreign (target) language 180. The MT 120utilizes an Interlingua-based translation approach. Instead of directlytranslating a document to another foreign language, the MT generator 123transforms the document into a language-independent, computer-readableform called Interlingua and then generates translations from theInterlingua text. As a result, translated documents will require nopostediting. A version of the MT 120 is created for each language andwill consist primarily of a set of knowledge sources designed to guidethe translation of Interlingua text to foreign language text. Inparticular, for every new target language, a new MT generator 123 mustbe individually developed.

When fully functional, the LE 130 will sometimes need to ask the author160 to choose from alternative interpretations for certain sentencesthat satisfy CSL grammatical constraints but for which the meaning isunclear. This process is known as disambiguation. After the LE 130 hasdetermined that a particular part of text uses only CSL vocabulary andsatisfies all CSL grammatical constrains, then the text will be labeledCSL-approved, pending this disambiguation. As explained below,disambiguation will not require any changes to the author-visibleaspects of the text. After the text has been disambiguated it will beready for translation into the target language 180.

In practice, the LE 130 is built as an extension to the text editor 140which provides the basic word processing functionality required byauthors and editors to create text and tables. The graphics editor 150is used for creating graphics. The graphics editor 150 provides a meansfor accessing the text labels on graphics through the text editor 140,so these text labels can be CSL-approved as well.

The LE 130 (via text editor 140) communicates with the MT analyzer 127and, through it, with the DM 137 during disambiguation via bidirectionalsocket-to-socket lines 132 and 133. In the preferred embodiment of thepresent invention, the DM is one of the knowledge bases that feeds theMT analyzer 127. The DM 137 is a symbolic representation of thedeclarative knowledge about the CSL vocabulary used by the MT analyzer127 and the LE 130.

FIG. 2 shows a high level flowchart of the operation of IATS 105. The MT120, LE 130, text editor 140, and graphics editor 150 are all controlledby the FMS 110. Control lines 111-113 provide the necessary controlinformation for proper operation of IATS 105.

Initially, the author 160 will use the FMS 110 to choose a document toedit, and the FMS 110 will start the text editor 140, displaying thefile for the specified document. Via the text editor 140, the authorenters text that may be unconstrained and ambiguous text into the IATS105, as shown in blocks 160 and 220. The author 160 will use standardeditor commands to create and modify the document until it is ready tobe checked for CSL compliance. Note that is it anticipated that authorswill mostly enter text that is substantially prepared with the CSLconstraints in mind. The text will then be modified by the author inresponse to system feedback, based on violations to the pre-determinedlexical and grammatical constraints, to conform to the CSL. This is, ofcourse, much more efficient than initially entering totallyunconstrained text. However, the system will operate properly even iftotally unconstrained text is entered from the start.

The author's communication with the LE 130 consists of mouse click orkeystroke commands. However, one should note that other forms of inputmay be used, such as but not limited to the use of a stylus, voice,etc., without changing the scope or function of the present invention.An example of an input is a command to perform a CSL check or to findthe definition and usage example for a given word or phrase.

The CSL text that may contain residual ambiguity or stylistic problemsis analyzed for conformity with CSL and checked for compliance with thegrammatical rules contained in the knowledge bases, as shown in block230. The author is provided feedback to correct any mistakes viafeedback line 215. Specifically, the LE 130 provides informationregarding non-CSL words and phrases and sentences to the author 160.Finally, the text is checked for any ambiguous sentences. The LE promptsthe author to select an appropriate interpretation of a sentence'smeaning. This process is repeated until the text is fully disambiguated.

Once the author has made all the necessary corrections to the text, andthe analysis phase 230 has completed, the disambiguated/constrained text240 is passed to the MT analyzer and interpreter 250. The interpreterresides in the MT analyzer 127 together with the syntactic part of theanalyzer and translates the disambiguated/constrained text 240 intointerlingua 260. The interlingua 260 is in turn translated by generatorblock 270 into the target text 280. As shown in FIG. 3, the interlinguatext 260 is in a form that can be translated to multiple targetlanguages 306-310.

By requiring and enabling the author to create documents that conform tospecific vocabulary and grammatical constraints, it is feasible toperform the accurate translation of constrained-language texts toforeign languages with no postediting required. Postediting is notrequired since the LE vocabulary check block 217 and analysis block 230have caused the author to modify and/or disambiguate all possiblyambiguous sentences and all non-translatable words from the documentbefore translation.

II. Detailed Description of the Functional Blocks

In a preferred embodiment, each author will have sole use of aDECstation with 32 Meg of RAM, a 400-megabyte disk drive, and a 19-inchcolor monitor. Each workstation will be configured for at least 100 Megof swap from its local disk. In addition to the authors' workstations,DECservers will be used as file servers, one for every two authoringgroups, for a total of no more than 45 users per file server.Furthermore, authoring workstations will reside on an Ethernet localnetwork. The system uses the Unix operating system (a Berkeley StandardDistribution (BSD) derivative is preferable to a System V (SYSV)derivative). A C programming language compiler and OSF/Motif librariesare available. The LE will be run within a Motif window manager. Itshould be noted that the present invention is not limited to the abovehardware and software platforms and other platforms are contemplated bythe present invention.

A. Text Editor

The preferred embodiment of the present invention provides a text editor140 which allows the author to input information that will eventually beanalyzed and finally translated into a foreign language. Anycommercially available word processing software can be used with thepresent invention. A preferred embodiment uses a SGML text editor 140provided by ArborText (ArborText Inc., 535 West William St., Ann Arbor,Mich. 48103). The SGML text editor 140 provides the basic wordprocessing functionality required by authors and editors, and is usedwith software by InterCap (of Annapolis, Md.) for creating graphics.

The present invention utilizes a SGML text editor 140 since it createstext using Standard Generalized Markup Language (SGML) tags. SGML is anInternational Standard markup language for describing the structure ofelectronic documents. It is designed to meet the requirements for a widerange of document processing and interchange tasks. SGML tags enabledocuments to be described in terms of their content (text, images, etc)and logical structure (chapters, paragraphs, figures, tables, etc.) Inthe case of larger, more complex, electronic documents, it also makes itpossible to describe the physical organization of a document into files.SGML is designed to enable documents of any type, simple or complex,short or long, to be described in a manner that is independent of boththe system and application. This independence enables documentinterchange between different systems for different applications withoutmisinterpretation or loss of data.

SGML is a markup language, that is, a language for "marking up" orannotating text by means of or by using coded information that adds tothe conventional textual information conveyed by a given piece of thetext. In most cases it takes the form of sequences of characters atvarious points throughout an electronic document. Each sequence isdistinguishable from the text around it by the special characters thatbegin and end it. The software can verify that the correct markup hasbeen inserted into the text by examining the SGML tags upon request. Themarkup is generalized in that it is not specific to any particularsystem or task. For a more in depth discussion of SGML tags seeInternational Standard (ISO) 8879, Information processing--Text andoffice systems--Standard Generalized Markup Language (SGML), Ref. No.ISO 8879-1986(E).

The following capabilities are possible due to the use of the SGML tags:

(1) dividing documents into fragments or translatable units. The texteditor 140 software uses both punctuation and SGML tags to recognizetranslatability units in the source input text (e.g., an SGML tag isnecessary to identify section titles);

(2) shielding (insulating) units that will not be translated. Althoughthe system is based on the premise that all words and sentences willbelong to the constrained language that cannot be predicted in advance(for example, names and addresses) or classes of vocabulary that cannot(readily) be exhaustively specified (for example, part numbers, errormessages from machinery). SGML tags can be put around these items toindicate to the system that they are exempt from checking;

(3) identifying contents (e.g., part number) as discussed in (2);

(4) allowing partial sentences to be translated (e.g., bulleted items);

(5) assisting in translating tables (one cell at a time) by identifyingstructure of text. This feature is similar to that described in (1);

(6) assisting the parsing process (described below) through (2), (3),(4), (5);

(7) assisting in disambiguation by providing a means of insertinginvisible tags into the source text so as to indicate the correctinterpretation of an ambiguous sentence;

(8) assisting in translating currencies and mathematical units throughthe identification of specific types of text that require specialtreatment.

(9) providing a means of labeling a portion of text as translatable. Inother words, certifying that a portion of text has advanced through theprocess outlined below and that the text is unambiguous constrained textthat can be translated without postediting.

In the past, authors have created (by way of the text editor 140)electronic documents (text only--no graphics) that represented acomplete "book." This implies that all work is done by one writer, andthat the information created is not easily reused. The presentinvention, however, compiles (or creates) books (manuals, documents)from a set of smaller pieces or information elements, which implies thatthe work can be done by multiple writers. The result of this inventionis enhanced reusability. An information element is defined as thesmallest stand-alone piece of service information about a specializeddomain. It should be noted, however, that although a preferredembodiment utilizes information elements, the present invention canproduce accurate, unambiguous translated documents without the use ofinformation elements.

FIG. 4 shows an example of an information element 410 which includes a"unique" heading 415, a "unique" block of text 420, a "shared" graphic430, a "shared" table 435, and a "shared" block of test 425.

"Unique" information is that information which applies only to theinformation element in which it's contained. This implies that the"unique" information is filed as part of the information element 450.

A "shared" object (a graphic, table, or block of text) is informationthat is "referenced" in the information element. The content of "shared"objects are displayed in the authoring tool but only "pointed to" in thefiled information element 450.

"Shared" objects differ from information elements in that they do notstand-alone (i.e., they do not convey enough information by themselvesto impart substantive information). Each "shared" object is in itself aseparate file as shown in block 450.

Information elements are formed by combining "unique" blocks ofinformation (text and/or tables) with one or more "shared" objects. Notethat "unique" heading 415 and "unique" text 420 is combined with"shared" graphic 430, "shared" table 435, and "shared" text 425. A setof one or more information elements make up a complete document (book).

"Shared" objects are stored in "shared" libraries. Library types include"shared" graphic libraries 460a, "shared" tables libraries 460b,"shared" text libraries 460c, "shared" audio libraries 460d, and"shared" video libraries 460e. A shared object is stored only one time.When used in individual information elements, only "pointers" to theoriginal shared object will be placed in the information shared file450. This minimizes the amount of disk space that will be required. Whenthe original object is changed, all those information elements that"point" to that object are automatically changed. A shared object can beused in any publication type.

A "shared information element" is an information element that is used inmore than one document. For example, the same four information elementsin release library 470 are used to create portions of documents 480 and485.

All communication between the author and the LE 130 will be mediated byan LE User Interface (UI), implemented as either an extension ofstandard SGML Editor facilities such as menu options, or in separatewindows. The UI provides and manages access to and control of the CSLcheckers and CSL vocabulary look-up, and it is the primary tool enablingusers to interact with the CSL LE. Although the term "user interface" isoften used in a more general sense to refer to the interface to anentire software system, here the term will be restricted to mean theinterface to the CSL checkers, vocabulary look-up facility, and thedisambiguation facility.

Among other things, the UI must provide clear information regarding (a)the actions the LE is taking, (b) the result of these actions, and (c)any ensuing actions. For example, whenever an action initiated throughthe UI introduces more than a very brief, real-time pause, the UI shouldinform the author of a possible delay by means of a succinct message.

The author can invoke LE functionality by choosing an option from apull-down menu in text editor 140. The available options allow theauthor to initiate and view feedback from CSL checking (both vocabularyand grammar checking) and from vocabulary look-up. The author canrequest that checking be initiated on the currently displayed documentor request vocabulary look-up on a given word or phrase.

The UI will clearly indicate each instance of non-CSL language found inthe document. Possible ways of indicating non-CSL language include theuse of color and changes to font type or size in the SGML Editor window.The UI will display all known information regarding any non-CSL word.For example, in appropriate cases the UI will display a message sayingthat the word is non-CSL but has CSL synonyms, as well as a list ofthose synonyms.

In cases where a Vocabulary Checker report includes a list ofalternatives to the non-CSL word in focus (for example, spellingalternatives or CSL synonyms), the author will be able to select one ofthose alternatives and request that it be automatically replaced in thedocument. In some cases, the author may have to modify (i.e., add theappropriate ending) the selected alternative to ensure that it is in theappropriate form.

When an author requests vocabulary information, the UI will displayspelling alternatives, synonyms, a definition, and/or a usage examplefor the item indicated.

The author can move quickly and easily between checker information andvocabulary look-up information inside the UI. This enables the author toperform information searches (e.g., synonym look-up) during the processof changing the documents to remove non-CSL language.

In most cases, the UI provides automatic replacement of non-CSLvocabulary with CSL vocabulary, with no need for the user to modify theCSL word to ensure that it is in the appropriate form. However, thereare some cases in which the vocabulary checker (described below), whichdoes no parsing of a document, will not be able to identify the correctform to provide. Consider the following caption, in the case where theverb "view" is not in CSL, but has the CSL synonym "see":

Direction of Crankshaft Rotation (when viewed from flywheel end)

The Vocabulary Checker will not know if "saw" or "seen" should beoffered as a synonym for "viewed." Of course, in this case a reasonablecourse of action might be to offer both possibilities and allow theauthor to choose the appropriate one. Because there is no certainty thatevery case will allow a presentation that enables the author to order adirect replacement. LE 130 provides a list of replacement options in thecorrect form where possible. There may be cases, though, when the authorwill find it necessary to edit a suggested CSL word or phrase beforerequesting that it be put into the document.

Finally, the LE UI provides support for disambiguating the meaning ofsentences. It does this by providing a list of possible alternativeinterpretations to the author, allows the author to select theappropriate interpretation, and then tags the sentence so as to indicatethat authors selection.

C. File Management System

The File Management System (FMS) 110 serves as the authors' interfacethe IE Release Library 470 and the SGML text editor 140. Typically,authors will select an IE to edit by indicating the file for that IE inthe FMS interface. The FMS 110 will then initiate and manage an SGMLEditor session for that IE. Finished documents will be forwarded to ahuman editor or Information Integrator via FMS-controlled facilities.

D. Constrained Source Language (CSL)

Given the complexity of today's technical documentation, high qualitymachine translation of natural language unconstrained texts ispractically impossible. The major obstacles to this are of a linguisticnature. The crucial process in translating a source text is that ofrendering its meaning in the target language. Because meaning lies underthe surface of textual signals, such overt signals have to be analyzed.The meaning resulting from this analysis is used in the process ofgenerating the signals of the target language. Some of the most vexingtranslation problems result from those features inherent in languagewhich hinder analysis and generation.

A few of these features are:

1. Words with more than one meaning in an ambiguous context

Example: Make it with light material.

[Is the material "not dark" or "not heavy"?]

2. Words of ambiguous makeup

Example: The German word "Arbeiterinformation" is either "informationfor workers" [Arbeiter+Information] or "formation of female workers"[Arbeiterin+Formation]

3. Words which play more than one syntactic role

Round may be a noun (N), a verb (V), or an adjective (A):

(N) Liston was knocked out in the first round.

(V) Round off the figures before tabulating them.

(A) Do not place the cube in a round box.

4. Combinations of words which may play more than one syntactic roleeach

Example: British Left Waffles on Falklands.

[If Left Waffles is read as N+V, the headline is about the British Left]

[If Left Waffles is read as V+N, the headline is about the British]

5. Combinations of words in ambiguous structures

Example: Visiting relatives can be boring.

[Is it the "visiting of relatives" or the "relatives who visit" whichcan be boring?]

Example: Lift the head with the lifting eye.

[Is the "lifting eye" an instrument or a feature of the "head"?]

6. Confusing pronominal reference

Example: The monkey ate the banana because it was . . .

[What does "it" refer back to, the monkey or the banana?]

Generation problems add to the above, increasing the overall difficultyof machine translation.

The magnitude of the translation problems is considerably lessened byany reductions of the range of linguistic phenomena the languagerepresents. A sublanguage covers the range of objects, processes andrelations within a limited domain. Yet a sublanguage may be limited inits lexicon while it may not necessarily be limited in the power of itsgrammar. Under controlled situations, a strategy aimed at facilitatingmachine translation is that of constraining both the lexicon and thegrammar of the sublanguage.

Constraints on the lexicon limit its size by avoiding synonyms, andcontrol lexical ambiguity by specializing the lexical units for theexpression of, as far as possible, one meaning per unit. It is easy toimagine how these restrictions would avoid the problems exemplified in1, 2, and 4, above. Grammatical constraints may simply rule outprocesses like pronominalization (6 above) or require that the intendedmeaning be made clearer either through addition or repetition ofotherwise redundant information or through rewrite. The followingexample sets the parameters for application of this requirement:

Unconstrained, ambiguous English (which can be interpreted as either A,B1, or B2 below):

Clean the connecting rod and main bearings.

Unambiguous English version A:

Clean the connecting rod bearings and the main bearings.

Unambiguous English version B1:

Clean the main bearings and the connecting rod.

Unambiguous English version B2:

Clean the main bearings and the connecting rods.

The number and types of lexical and grammatical constraints may varywidely depending on the purpose of development of the constrainedsublanguage.

In view of the above, the present invention limits the authoring ofdocuments within the bounds of a constrained language. A constrainedlanguage is a sublanguage of a source language (e.g., American English)developed for the domain of a particular user application. For adiscussion generally of constrained or controlled languages see Adriaenset al, From COGRAM to ALCOGRAM: Toward a controlled English GrammarChecker, Proc. of Coling-92, Nantes (Aug. 23-28, 1992) which isincorporated by reference. In the context of machine translation, thegoals of the constrained language are as follows:

1. To facilitate consistent authoring of source documents, and toencourage clear and direct writing; and

2. To provide a principled framework for source texts that will allowfast, accurate, and high-quality machine translation of user documents.

The set of rules that authors must follow to ensure that the grammar ofwhat they write conforms to CSL will be referred to as CSL GrammaticalConstraints. The computational implementation of CSL grammaticalconstraints used to analyze CSL texts in the MT component will bereferred to as the CSL Functional Grammar, based on the well knownformalisms developed by Martin Kay and later modified by R. Kaplan andJ. Bresnan (see Kay, M., "Parsing in Functional Unification Grammar," inD. Dowty, L Karttunen and A. Zwicky (eds.), Natural Language Parsing:Psychological. Computational. and Theoretical Perspetives, Cambridge,Mass.: Cambridge University Press, pgs. 251-278 (1985) and Kaplan R. andJ. Bresnan, "Lexical Functional Grammar: A Formal System for GrammaticalRepresentation," in J. Bresnan (ed.), The Mental Representation ofGrammatical Relations, Cambridge, Mass.: MIT Press, pgs. 172-281 (1982)both of which are incorporated by reference.

In the rest of this document, we refer frequently to the notion that aword or phrase may be "in CSL" or "not in CSL." Below we will describethe assumptions about the type of vocabulary restrictions that will beimposed by CSL and to clarify the use of the expression "in CSL."

The same word or phrase in English can have many different meanings; forexample, a general purpose dictionary may list the following definitionsfor the word "leak":

(1) verb: to permit the escape of something through a breach or flaw;

(2) verb: to disclose information without official authority orsanction; and

(3) noun: a crack or opening that permits something to escape from orenter a container or conduit.

Each of these different meanings is referred to as a "sense" of the wordor phrase. Multiple senses for a single word or phrase can causeproblems for an MT system, which doesn't have all the knowledge thathumans use to understand which of several possible senses is intended ina given sentence. For many words, the system can eliminate someambiguity by recognizing the part of speech of the word as used in aparticular sentence (noun, verb, adjective, etc.). This is possiblebecause each definition of a word is particular to the use of that wordas a certain part of speech, as indicated above for "leak."

However, to avoid the kinds of ambiguity that the MT 120 cannoteliminate, the CSL specification strives to include a single one senseof a word or phrase for each part of speech. Thus, when a word or phraseis "in CSL," it can be used in CSL in at least one of its possiblesenses. For example, an author writing in CSL may be allowed to use"leak" in senses (1) and (3) above, but not in sense (2). Saying that aword or phrase is "in CSL" does not mean that all possible uses of theword or phrase can be translated.

If a word or phrase is in CSL, then all forms of that word or phrasethat can express its CSL sense(s) are also in CSL. In the above example,an author may use not only the verb "leak" but also the related verbforms "leaked," "leaking" and "leaks." If a word or phrase with a nounsense is part of CSL, both its singular and plural forms may be used.Note, however, phrases which function as more than one part of speechare uncommon. This heuristic is therefore less relevant in the case ofan ambiguous phrase.

A vocabulary is the collection of words and phrases used in a particularlanguage or sublanguage. A limited domain will be referred to by meansof a limited vocabulary which is used to communicate or expressinformation about a limited realm of experience. An example of a limiteddomain might be farming, where the limited vocabulary would includeterms concerning farm equipment and activities. The MT component willoperate on more than one kind of vocabulary. The words and phrases formachine translation will be stored in the MT lexicon. The vocabulary canbe divided into different classes: (1) functional items; (2) generalcontent items; and (3) technical nomenclature.

Functional items in English are the single words and word combinationswhich serve primarily to connect ideas in a sentence. They are requiredfor almost any type of written communication in English. This classincludes prepositions (to, from, with, in front of, etc.), conjunctions(and, but, or, if, when, because, since, while, etc.), determiners (the,a, your, most of), pronouns (it, something, anybody, etc.), some adverbs(no, never, always, not, slowly, etc.), and auxiliary verbs (should,may, ought, must, etc.).

General content words are used in large measure to describe the worldaround us; their main use is to reflect the usual and common humanexperience. Typically, documents focus on a very specialized part of thehuman experience (e.g., machines and their upkeep). As such, the generalvocabulary will be relatively restricted for MT.

The technical nomenclature comprises technical content words andphrases, and user application specific vocabulary. Technical contentitems are words and phrases which are specific to a particular field ofendeavor or domain. Most technical words are nouns, used to name items,such as parts, components, machines, or materials. They may, however,also include other classes of words, such as verbs, adjectives, andadverbs. Obviously, as these words are not used in common, everydayconversation, they contrast with general content words.

Technical content phrases are multiple-word sequences built up from allthe preceding classes. These phrases are the most characteristic form oftechnical documentation vocabulary. The user application specificvocabulary is the part of the terminology that contains distinctly userapplication created words and complex terms. These include thefollowing: product names, titles of documents, acronyms used by theuser, and from numbers.

The development of a useful and complete vocabulary is important for anydocumentation effort. When documentation is subsequently translated, thevocabulary becomes an important resource for the translation effort. TheMT 120 is designed to handle most functional items available in English,except those referring to very personal (I, me, my, etc.) orgender-based (hers, she, etc.) or other pronominal (it, them, etc.)usage. This will include a number of technical "borrowings" from Englishgeneral words (such as "truck" or "length"). The vast majority of theconstrained language vocabulary, then, will consist of the "special"(e.g., technical) terms of one or more words, which express the objectsand processes of the special domain. To the extent that the vocabularyis able to express the full range of notions about the special domain,the vocabulary is said to be complete.

The development of a streamlined but complete vocabulary contributesgreatly to the success of the IATS system 105. The constrained language,by specifying proper and improper use of vocabulary, will assure thatthe documents can be produced in a manner conducive to fast, accurate,and high-quality machine translation.

Vocabulary items should reflect clear ideas and be appropriate for thetarget readership. Terms which are sexist, colloquial, idiomatic, overlycomplicated or technical, obscure, or which in other ways inhibitcommunication should be avoided. These and other generally acceptedstylistic considerations, while not necessarily mandatory forMT-oriented processing, are nevertheless important guidelines fordocument production in general.

It should be noted that although the bulk of the discussion in thisdocument concerning the constrained source language and/or language ingeneral centers around American English, analogous comparisons can bemade in connection with all other languages. There is nothing inherentabout the system 100 described herein that requires American English tobe the source language. In fact, the system 100 is not designed to workwith American English as the only source language. However, thedatabases (e.g., the domain model) that interact with the LE 130 and MT120 will have to be changed to correspond to the constraints of theparticular source language.

The rules of standard American English orthography must be followed.Non-standard spellings, such as "thru" for "through," "moulding" for"molding," or "hodometer" for "odometer" are to be avoided. Capitalizedwords (e.g., On-Off, Value Planned Repair) should only be used toindicate special meaning of terms. These terms must be listed in theuser application vocabulary. Such is also the case for non-standardcapitalization usage (BrakeSaver). Likewise, abbreviations, when used(ROPS, API, PIN), must be listed in the user application specificvocabulary. The format for numbers, units of measurement, and dates mustbe consistent.

Constrained language recovery items should also be used according totheir constrained language meaning. In doing so, the writer assures thatthe MT always translates a word by using the proper constrained languageword sense. Some English words can also belong to more than onesyntactic category. In the constrained language, all syntacticallyambiguous words should be used in constructions that disambiguate them.

One difficult problem arising from the special nature of the domain is,in some fields, the frequent use of lengthy compound nouns. Themodification relationships present in such compound nouns are expresseddifferently in different languages. Since it is not always feasible torecover these relationships from the source text and express them in thetarget language, complex compound nouns with the followingcharacteristics may be listed in the MT lexicon:

Technical terms from the user application specific vocabulary; and

Compound terms consisting of more than one word.

Complicated noun-noun compounding should be avoided, if possible.However, with some items listed in the lexicon, the MT is capable ofhandling this important characteristic of documentation. Note thatnoun-noun compounding which is a very common feature of the Englishlanguage, may not necessarily be a common feature of other language, andas such, the constraints under which the constrained language is createddiffers which the particular source language being utilized.

English is very rich in verb-particle combinations, where a verb iscombined with a preposition, adverb, or other part of speech. As theparticle can often be separated from the verb by objects or otherphrases, this causes complexity and ambiguity in MT processing of theinput text. Accordingly, verb-particle combinations should be rewrittenwherever possible. This can usually be accomplished by using asingle-word verb instead. For example, use:

"must" or "need" in place of "have to";

"consult" in place of "refer to";

"start the motor" in place of "turn the motor on";

Full terms and ideas should be used wherever possible. This isparticularly important where misunderstandings may arise. For example,in the phrase:

"Use a monkey wrench to loosen the bolt. . . ."

the word wrench must not be omitted. While most technically capablepeople would understand the implication without this word, it must berendered explicit during the translation process. CTE text must havevocabulary which is explicitly expressed wherever possible;abbreviations or shortened terms should be rewritten into lexicallycomplete expressions.

Consider another example:

"If the electrolyte density indicates that . . ."

Here the meaning is more explicit and complete when the idea is fullyexpressed:

"If measurement of the electrolyte density indicates that . . . "

Finally, in the following sentences have words or phrases missing, theunderlined words are supplied to make the meaning more redundant:

Turn the start switch key to OFF and remove the key.

Pull the backrest (1) up, and move the backrest to the desired position.

Jump starting: make sure the machines do not touch each other.

When such "gaps" are filled, the idea is more complete and a meaningfultranslation by IATS 105 becomes more certain. Translation errors due togaps are a common reason for postediting. Hence, gaps are disallowed.

Colloquial or spoken English often favors the use of very general words.This may sometimes result in a degree of vagueness which must beresolved during the translation process. For example, words such asconditions, remove, facilities, procedure, go, do, is for, make, get,etc. are correct but imprecise.

In a sentence like:

When the temperature reaches 32° F., you must take special precautions.

the word "reaches" does not communicate whether the temperature isdropping or rising; one of these two terms would be more exact here, andthe text just as readable.

Some languages make distinctions where English does not always do so;for example, we say oil for either a lubricating fluid, or one used forcombustion; we say fuel whether or not it is diesel. Similarly, when theword door is used in isolation, it is not always possible to tell whatkind of door is meant. A car door? A building door? A compartment door?Other languages may need to make these distinctions. Wherever possible,full terms should be used in English.

D. Domain Model

Knowledge-based Machine Translation (KBMT) must be supported by worldknowledge and by linguistic semantic knowledge about meanings of lexicalunits and their combinations. A KBMT knowledge base must be able torepresent not only a general, taxonomic domain of object types such as"car is a kind of vehicle," "a door handle is a part of a door,"artifacts are characterized by (among other properties) the property"made-by"; it must also represent knowledge about particular instancesof object types (e.g., "IBM" can be included into the domain model as amarked instance of the object type "corporation") as well as instancesof (potentially complex) event types (e.g., the election of George Bushas president of the United States is a marked instance of the complexaction "to-elect"). The ontological part of the knowledge base takes theform of a multihierarchy of concepts connected through taxonomy-buildinglinks, such as is-a, part-of, and some others. We call the resultingstructure a multihierarchy because concepts are allowed to have multipleparents on each link type.

The domain model or concept lexicon contains an ontological model, whichprovides uniform definitions of basic categories (such as objects,event-types, relations, properties, episodes, etc.) used as buildingblocks for descriptions of particular domains. This "world" model isrelatively static and is organized as a multiply interconnected networkof ontological concepts. The general development of an ontology of anapplication (sub)world in is well known in the art. See, for example,Brachman and Schmolze, An Overview of the KL-ONE KnowledgeRepresentation System, Cognitive Science, vol. 9, 1985; Lenat, et al,Cyc: Using Common Sense Knowledge to Overcome Brittleness and KnowledgeAcquisition Bottlenecks, AI Magazine, VI:65-85, 1985; Hobbs, Overview ofthe Tacitus Project, Computational Linguistics, 12:3, 1986; andNirenburg et al, Acquisition of Very Large Knowledge Bases: Methodology,Tools and Applications, Center for Machine Translation, Carnegie MellonUniversity (1988) all of which are incorporated herein by reference.

The ontology is a language-independent conceptual representation of aspecific subworld, such as heavy equipment troubleshooting and repair orthe interaction between personal computers and their users. It providesthe semantic information necessary in the sublanguage domain for parsingsource text in interlingua text and generating target texts frominterlingua texts. The domain model has to be of sufficient detail toprovide sufficient semantic restrictions that eliminate ambiguities inparsing, the ontological model must provide uniform definitions of basicontological categories that are the building blocks for descriptions ofparticular domains.

In a world model, the ontological concepts can be first subdivided intoobjects, events, forces (introduced to account for intentionless agents)and properties. Properties can be further subdivided into relations andattributes. Relations will be defined as mappings among concepts (e.g.,"belongs-to" is a relation, since it maps an object into the set {*human*organization}), while attributes will be defined as mappings ofconcepts into specially defined value sets (e.g., "temperature" is anattribute that maps physical objects into values on the semi-open scale[0,*], with the granularity of degrees on the Kelvin scale). Conceptsare typically represented as frames whose slots are properties fullydefined in the system.

Domain models are a necessary part of any knowledge-based system, notonly a knowledge-based machine translation one. The domain model is asemantic hierarchy of concepts that occur in the translation domain. Forinstance, we may define the object *O-VEHICLE to include*O-WHEELED-VEHICLE and *O-TRACKED-VEHICLE, and the former to include*O-TRUCK, *O-WHEELED-TRACTOR, and so on. At the bottom of this hierarchyare the specific concepts corresponding to terminology in CSL. We callthis bottom part the shared K/DM. In order to translate accurately wemust place semantic restrictions on the roles that different conceptsplay. For instance, the fact that the agent role of an *E-DRIVE actionmust be filled by a human is a semantic restriction placed on*O-VEHICLE, and automatically inherited by all types of vehicles (thussaving repetitious work in hand coding each example). The Authoring partof the domain model augments the K/DM with synonyms not in CSL and otherinformation to provide useful feedback to the author as he or shecomposes each information element.

FIG. 5 conceptually illustrates the Domain Model (DM) used by thepresent invention. The DM 500 is a representation of the declarativeknowledge about the CSL vocabulary used by the MT 120 and the LE 130.The DM 500 is made up of three distinct parts:

1. A Kernel Domain Model (K/DM) 510 contains all lexical informationthat is required by both the MT analyzer 127 and the LE 130; inparticular, the kernel includes all CSL lexical items (words andphrases) with associated semantic concepts, parts of speech,morphological information, etc.

2. A MT Domain Model (MT/DM) 520 which contains information that isrequired only by the MT analyzer 127. The MT Domain Model is thehierarchy of concepts used for unambiguous mapping and semanticverification in translation. It includes selectional restrictions onconcepts and a hierarchical classification of concepts.

3. A LE Domain Model (LE/DM) 530 contains information that is requiredonly by the LE 130; this includes non-CSL synonyms for CSL lexicalitems, dictionary definitions of CSL lexical items, and examples of theCSL lexical items in use.

The Kernel/DM 510 will contain one lexical entry for every CSL lexicalitem (word or phrase). (A "lexical entry" consists of a lexical item--aword or phrase--and minimally its associated semantic concept and partof speech), for example, if the word "leak" is in CSL as both a noun anda verb, it would have two lexical entries.) Each lexical item will bedated with additional information required by the LE 130 and/or the MT140 such as a definition and irregular morphological variants.

The shared K/DM 510 speeds up refinements and extensions of the CSL,saves duplication of effort in the authoring and translation components,and provides a human readable structure to facilitate maintenance andextensions.

The K/DM 510 is a lexicon containing both the syntactic and semanticinformation about terms (words and phrases) in the constrained languagetext. It is the central lexical knowledge source for the analysis sideof the automated machine translation (MT) process. The K/DM 510 is alsoused as the basis for the LE/DM.

The K/DM 510 includes a separate entry for each term in each syntacticcategory. (Thus, for a word like "truck," which is both a noun and averb, there are two entries.) K/DM entries contain the followinginformation:

root (e.g., "truck");

part of speech (e.g., N);

for content words, the concept or meaning (e.g., O-TRUCK);

morphological information (e.g., irregular inflections);

syntactic information (e.g., whether a noun is count or mass);

definitional information: short definitions and textual examplesdocumenting the different senses and uses of the words, and aspecification of the sense in which the word is to be used in theconstrained language.

The DM 500 is defined in three sets of external human-readable fileswhich can be read by the process(es) that require their use. Since theMT 120 and the LE 130 will be running in separate processes, theinformation in the model is represented internally in two forms: one forthe parts of the DM required by the MT 120 and another for the partrequired by the LE 130. So the K/DM 510 is defined in a set of fileswhich can be represented in both forms; the LE/DM 530 is onlyrepresented in the form used by the LE 130; and the MT/DM 520 is onlyrepresented in the form used by the MT 120. Described below are theexternal file formats, the content of the various parts of the DM, andthe internal representation of the information used by the LE 130.

Once again, the K/DM contains all information required by both the MT120 and the LE 130. This includes a CSL lexical item--the base word,phrase, or quoted term and a semantic concept--the semantic conceptassociated with the lexical item, represented in a lexical entry by a"concept name." Further, it includes a part of speech--one of a fixedset of parts of speech (e.g., verb, adjective, etc.), a definition--arough definition for general vocabulary terms, to clarify which ofseveral senses a CSL lexical item may have, and irregular morphologicalvariants--a listing of irregular morphological forms and the name of themorphological transformations for each. Examples of names ofmorphological transformations for verbs are "past", "third personsingular present", "past participle", "present participle". The value ofthis field for the word "drive", for example, would be ((past drove)(past-participle driven)), indicating that those two forms of the verbsare irregular and all other forms are regular. Finally, the K/DMincludes typographical restrictions--e.g., if the lexical item must bein all capitals, have the first character capitalized, etc.

The MT/DM 520 contains information required only by the MT 120. Thisincludes: selectional restrictions on concepts and hierarchicalclassification of concepts for organization and inheritance ofselectional restrictions.

The LE/DM 530 will contain non-CSL synonyms to help the authors tochoose valid CSL lexical items. Together, the Kernel and the LE/DM willcontain all information and all restrictions required to characterizethe CSL lexicon in support of the LE Vocabulary Checker (describedbelow). The LE/DM contains additional information required only by theLE Vocabulary Checker. This includes: a dictionary definition--thedefinition of the word or phrase that will be presented to authors bythe LE, non-CSL synonyms--synonyms for the CSL lexical items thatauthors might use in writing documents, and a usage example--an exampleof the word or phrase in a CSL sentence, for presentation to the authorsby the LE.

The purpose of including this information in the LE/DM is to help theauthors ensure that their writing is made up of valid CSL words andphrases. The dictionary definitions and usage examples will help theauthors ensure that they are using a word or phrase of a part of speechand with a meaning that is permitted in CSL; however, dictionarydefinitions or usage examples will not be required for every CSL lexicalitem. Rather, they will be required only for the small percentage ofambiguous or vague terms whose CSL meaning will not be immediately clearto authors. This probably amounts to less than half of the lexical itemsin the DM. For example, function words like "for" and "the" will notrequire definitions or examples; many technical terms, especially thosewith very specific technical meanings, may not require definitions orexamples either.

The non-CSL synonyms in the LE/DM will help authors who write a non-CSLword or phrase to choose a synonymous or related CSL word or phrase withwhich to replace it. It is desirable for the vocabulary checker toprovide information about not only synonyms which are the same part ofspeech as the non-CSL word with which they are synonymous, but alsoabout related words that might aid authors in rewording sentences. Ifthe latter are included, the LE/DM must contain information about theserelated words in addition to the mandatory content.

E. Language Editor

Referring to FIG. 1(b), the constrained language editor (LE) 130 is aset of tools to support authors and editors in creating documents withinthe bounds of CSL. These tools will help an author to use theappropriate CSL vocabulary and grammar to write service documentation.The LE 130 is built as an "extension" of the SGML text editor 140.Although the LE 130 uses the same communication channels as the SGMLtext editor 140, the functions of the two are mutually exclusive.However, the user interface used to interact with the LE 130 is a"seamless extension" of the SGML text editor interface.

The author 160 creates documents in the SGML text editor 140 and invokesthe LE 130 it. The LE 130 informs the author whether individual words ina document are non-CSL, and will be able to suggest synonyms in CSL forwords that are relevant to the user application information domain, butare not in CSL. In addition, the LE 130 tells the author whether or notthe text in a file satisfies CSL syntactic constraints.

The LE 130 software includes the following: a Vocabulary Checker, aGrammar Checker, including an interface through the MT SyntacticAnalyzer, which will provide the core grammar checking functionality,and a User Interface (UI). In addition, the CSL vocabulary informationused by the CSL LE will be represented in the K/DM and the LE/DM.

The LE 130 will certify that all vocabulary and sentence structures in adocument conform to the CSL specification. The LE 130 marks the documentwith an SGML tag that represents this CSL approval. Checking must beperformed on all text in a document, which includes the following:sentences, headings, list items, captions, call-outs in graphics, andinformation in tables.

Since the present invention is based on the premise that authors shouldbe productive as possible during a CSL checking session, and thatauthors should not have to work multiple authoring documents at once, abatch mode of operation, which requires a user to submit a document forprocessing and wait until the entire document is finished before he orshe gets any feedback, is not appropriate. The LE 130 provides aninteractive mode of operation for both vocabulary checking, grammarchecking, and interactive disambiguation.

FIG. 6 shows a high level flow chart of the operation of the LE 130. TheLE 130 takes in as input text 605, which may be ambiguous andunconstrained. The potentially ambiguous unconstrained input text 605 isfirst checked with a vocabulary checker 610 which performs its functions(as described below) with the aid of a spell checker 615. (The servicesof the spell checker happen to be rendered in this embodiment by thespell checker regularly featured by the host TE 140.) Once thevocabulary checker 610 has completed its check and made all necessarycorrections (with the aid of the author) then the lexically constrainedtext 617 is supplied to a grammar checker 620. The grammar checker 620produces syntactically correct CSL text 625. The constrainedsyntactically correct text 625 is then disambiguated, as shown in block630. The result of the disambiguation is translatable unambiguousconstrained text 635. The translatable text 635 can be translated into aforeign language without any pre-editing required. The accuracy of theresulting translation also makes postediting unnecessary.

1. Vocabulary Checker

FIG. 7 shows a flow chart of the operation of vocabulary checker 610.The vocabulary checker 610 identifies words not known to be CSL. Thevocabulary checker 610 identifies occurrences of non-CSL words, in anauthor's text, and helps an author find valid CSL replacements fornon-CSL words. It recognizes word boundaries in a document andidentifies every instance of a lexical item that is not known to be CSL.

As shown in block 706, the first term of a unit is selected to bechecked. The term is then checked, as shown in block 710, against a CSLlexical database (i.e., dictionary) which contains all CSL words. If theterm is not found in the CSL dictionary, the term is then spell checkedagainst a standard dictionary, as shown in block 722. If the word hasbeen misspelled, the author is provided a means of correcting thespelling mistake (i.e., the vocabulary checker 610 displays spellingalternatives), as shown in block 726.

The item is then checked to determine whether it is in the CSLvocabulary, as shown in block 734. If the item is in the CSL vocabulary,then the procedure advances to block 718. However, if the item is not inthe CSL vocabulary, the system checks to see if the LE/DM contains asynonym for the item being checked, as shown in block 736. If at leastone synonym exists in the LE/DM, the system displays the synonym(s)which are part of the CSL vocabulary and allows the author to make aselection, as shown in block 738. However, should the LE/DM not have asynonym for the item under checking, the author has the opportunity torework her input, as shown in block 740. The outcome of this rework goesback to block 710. Once a legal selection has been made by the author,the procedure 700 then proceeds to block 718.

When a non-CSL word is identified, the author has the following options:she can select an alternative and substitute it for the word in thedocument, or she can enter a new item and substitute it for the word inthe document. Typically, the author selects one of the synonyms toreplace the non-CSL item. If the author should decide to skip theproblem, the lack of resolution would result in failure of the text tobe approved as CSL.

Block 718 checks to determine whether there are any more terms in theunit. If there are no more terms the procedure 700 stops. Otherwise thenext term is selected, as shown in block 714, and the procedure 700begins again from block 710.

In particular, the Vocabulary checker 610 identifies every instance of alexical item that is not known to be CSL. For each such word, thevocabulary checker 610 will determine which of the followingdescriptions is applicable and report supporting information to the userinterface as listed below:

a non-CSL word having known CSL synonyms; in this case the VocabularyChecker 610 will identify the synonyms. For instance, let us assume thatthe word "let" is non-CSL--

Author's Input, When Checked: Open the valve and let more nitrogen go tothe accumulator.

VC Message: The term is non-CSL, but there are related CSL alternatives.

CSL Alternatives: allow, allowed, enable, enabled, permit, permitted,leave, left

CSL Sentence as Edited: Open the valve and allow more nitrogen to go tothe accumulator.

a word which may only appear in CSL as part of a phrase, but which isnot used in a CSL phrase in the current context; in this case theVocabulary Checker 610 will report acceptable CSL phrases containing theword--

Author's Input, When Checked: The first time the valve lash is checked,the injector timing should be checked.

VC Message: The term is used in a non-CSI, context.

CSL Alternatives: advance signal timing, advance timing groove, timinggear, timing mechanism

CSL Sentence as Edited: The first time the valve lash is checked, theinjector timing mechanism should be checked.

a word or phrase which must appear within double quotation marks in CSL,but which is not enclosed in quotation marks in the current context; inthis case the Vocabulary Checker 610 will report that the term should bequoted--

Author's Input, When Checked: For more details, read the Testing andAdjusting article in the next section.

VC Message: This term is generally enclosed by quotes.

CSL Alternative: None

CSL Sentence as Edited: For more details, read the "Testing andAdjusting" article in the next section.

a word or phrase which must appear with specific, mandatorycapitalization in CSL, but which lacks that capitalization in thecurrent context (e.g., an acronym presented in lower case); in this casethe Vocabulary Checker 610 will report the correct CSL form(s)--

Author's Input, When Checked: Turn the screw until the pressure gaugereads 0 kpa (0 psi).

VC Message: The term is improperly capitalized.

CSL Alternative: kPa

CSL Sentence as Edited: Turn the screw until the pressure gauge reads 0kPa (0 psi).

a non-word (that is, a group of letters representing a misspelled word)that has known spelling alternatives; in this case the VocabularyChecker 610 will identify the spelling alternatives, regardless ofwhether the result is in CSL (the user will resubmit the chosenalternative for further checking)--

Author's Input, When Checked: When it is necessary to raise the boom,the boom must have correct support.

VC Message: The term is non-CSL.

CSL Alternative: necessary

CSL Sentence as Edited: When it is necessary to raise the boom, the boommust have correct support.

a word that is not in CSL and about which the system knows nothing. Themessage for an unknown word or phrase gives the author the opportunityto change the wording altogether or shield the illegal expression fromchecking, as the case may require. In the following example, the authoruses an SGML tag to tell the system to overlook the offensive languageand leave it intact--

Author's Input, When Checked: Put approximately 0.9 L (1 quart) ofSAE10W hydraulic oil in the nitrogen end of the accumulator.

VC Message: The term is unknown.

CSL Alternative: None

CSL Sentence as Edited: Put approximately 0.9 L (1 quart) of<sic>SAE10W</sic>hydraulic oil in the nitrogen end of accumulator.

a punctuation mark or special symbol that is not allowed in CSL in anycontext.

In cases where a non-CSL word has no direct CSL synonyms (that is, wordsthat could replace it directly in a document), the system can identifyrelated CSL words or phrases which an author could use to express theintended idea. This functionality provides authors with additionalsupport in rewording a sentence to include only CSL vocabulary. However,changes to use these related words could not be completed with theautomatic replacement facility provided for synonyms, since the changeswould require some modifications to the sentence structure. For example,if "can" was in CSL and "capable" was not, an author who wrote thefollowing sentence

The system is capable of being programmed for several customer-specifiedparameters.

would be told that "capable" [[capable]] was not a CSL word. Althoughthe word "can" [[can]] is CSL, neither the word "capable" nor the phrase"is capable of" [["is capable of"]] can be directly replaced with "can"without the need for further changes to the sentence.

2. Grammar Checker

The purpose of the Grammar Checker is to identify places where anauthor's text does not conform to CSL grammatical restrictions, and tofocus the author's attention on those places. The grammar checker 620functionality will be provided by the Analysis module 127 of the MTsystem 120, extended to allow the system to report instances ofsyntactic and semantic ambiguity. The grammar checker interface allowsthe author to respond interactively to requests for clarification ofambiguity. It is possible that a sentence can be a constrained languagebut that it may have more than one interpretation. The grammar checkerinterface will present some indication of the two or more possiblemeanings of the sentence to the author and request clarification. Anexample of an ambiguous sentence would be: "Check the cylinders on theinside." Are the cylinders located on the inside or are you supposed tocheck the inside of the cylinders? There are two kinds of possibleambiguities:

Lexical ambiguities. Lexical ambiguities occur where a word can have oneor more meanings in the constrained language. While it is a desirablethat in the constrained language each word should have only one meaningper part of speech, there are some words which will have more than onemeaning. For example, the word "gas" can have the meaning "natural gas"or "gasoline."

At the lexical level, too, the problem may be caused by one word whichcan be used in two different syntactic roles in CSL. Such is the case of"fuel", which can be either a noun or a verb in CSL. When the authorinputs a sentence where the syntactic role is not clear, the GrammarChecker (GC) 620 may prompt the author as follows.

Author's Input, When Checked: The sensor is attached to fuel rack.

GC Message: The term may be used as a noun or as a verb.

At this point, the author has the option of editing the sentence withouthelp from the system (which simply requires rewriting and submittingagain to the checker). If the author opts to request for help, thesystem may offer specific instructions to deal with problems of the sametype. In this case the help is specific:

Help!

GC Message: If the word is a noun, you may want to use a determinerbefore it. If it is a verb, can a determiner after it help? Example: Theship sinks vs. Ship the sinks.

The author then proceeds to edit the sentence and submits it to thegrammar checker 620 again.

Structural ambiguity. Structural ambiguity occurs where words in asentence may group together in more than one way. For example: "Removethe valve with the lever." Does the phrase "with the lever" from a unitwith the phrase "the valve," or does it, instead, from a unit with theverb "remove"? In other words, is this a sentence about a valve that hasa lever attached to it or is it about using a lever to remove a valve?

In the IATS 105, the component designed to answer this question is thedomain model 137, which is constructed in such a way as to minimize theoccurrence of such ambiguities.

As shown in FIG. 5, the DM/MT 520, which supports exclusively themachine translation process, contains two types of information. On theone hand, the semantic information (A) supports the identification ofrelationships between concepts. On the other hand, the contextualinformation (B) specifies for a particular verb the so-called deep casesor arguments that such verb can take. In the example underconsideration, let us consider first how the semantic information (A)and the contextual information (B) help the analyzer 127 determine thegrammatical structure of "Remove the valve with the lever".

Among many semantic relationships, there is a relationship "is a partof" which obtains, for instance, between the concept "hat" and theconcept "costume", where the "hat" "is a part of" the "costume". Thesame relationship obtains between the concept "sole" and the concept"shoe", "heel" and "shoe", etc. The semantic information (A) held in theDM/MT 520 identifies this and other semantic relationships between theconcepts in the domain.

When the process in the MT analyzer 127 goes to the DM/MT 520 forsemantic information concerning the relationship between the concept"valve" and the concept "lever". The information in the DM 137 will notenable the MT analyzer 127 to tell whether "lever" "is a part of""valve"--the knowledge about such relationship is just not there. So theMT analyzer 127 is still at a loss as to whether the phrase "with thelever" should be attached to the word "valve".

Now when the MT analyzer 127 turns to the contextual information (B), itfinds that the verb "remove" takes three cases: a nominative (NOM), anaccusative (ACC), and an instrumental (INS) (at a deeper level ofanalysis, however, than that of the Latin grammar of our school days).That is, "remove" fits in the following case frame.

₋ - - verb (NOM, ACC, INS)

Based on this abstract pattern, we can build sentences such as thefollowing.

    ______________________________________                                        NOM         VERB        ACC      INS                                          ______________________________________                                        The workman removed     the sand with a shovel                                Peter       has removed the box  with the nail                                etc.                                                                          ______________________________________                                    

As the DM/MT contains information about the combination of thepreposition to "with" and nouns hav ing the semantic feature[+INSTRUMENT]. such combination form instrumental phrases. Thisinformation enables the analyzer to determine that

a ) since "lever" is [+INSTRUMENT], "with the lever" is INS;

b) since "remove" can take the INS case, the phrase "with the lever"attaches to, fits together with, and is interpreted as modifying"remove".

Yet the DM 137 can only be as rich as we build it. In those cases wherethe semantic information has not been developed as fully as possible,the lexical entries in the domain may not be able to support thedisambiguation process performed by the MT analyzer 127.

Consider the case of "nail" in "Peter has removed the box with thenail". If the DM 137 contains the information about nails being part ofa wooden frame but fail to contain the information that nails are[+INSTRUMENT], then the MT analyzer 137 cannot possibly determinewhether "with" combines with "nail" to form an instrumental phrase. Theanalyzer being unable to resolve the structural ambiguity, the authorwill be asked to resolve it. When the text submitted by the authorundergoes grammar checking, the following interaction occurs.

Author's Input, When Checked: Peter has removed the box with the nail.

grammar checker 620 Message: The sentence is ambiguous.

1. Is the nail an instrument?

2. Does the "box" have a "nail"?

Once the author makes an interpretation choice, the checker attaches aninvisible SGML tag to the sentence, which indicates to the system howthe sentence should be translated.

As mentioned above, the MT analyzer 127 is called by the grammar checkerin order to check whether input text or an IE (or part thereof) conformsto the grammatical and semantic constraints of CSL. In this regard, apreferred embodiment returns a strict "green-light, red-light" messagefor each sentence, the latter indicating that the author must correctthe composition of the flagged sentences via the authoring environment.Once the entire input text or IE has been certified as CSL compliant itmay be stored away or sent for immediate translation.

Referring to FIG. 8, a high level flow chart of the grammar checker 620(syntactical analysis) and disambiguation checker 630 (semanticanalysis) is shown. The word "sentence" is used below to refer to theunit of text that passes or fails the checking by the analysis module127. The unit that is checked may actually be a non-sentential textcomponent such as a heading, title, or list element, or a caption orother text from a graphic. The grammar checker 620 recognizes sentenceboundaries and SGML element boundaries in an SGML marked-up text. Itidentifies every sentence that does not conform to the CSLspecification. This will include every sentence which cannot besuccessfully parsed by the MT Analysis module 127. The parsing may failfor reasons including but not limited to those listed below.

The sentence includes grammatical constructions which the analysismodule 127 will not parse. Such is the case, for instance, when thesentence contains a reduced relative clause. The reduction results fromdeleting the relative pronoun "that" and the verb "be" in a sentencelike "Don't change the values that are programmed into the unit".

Author's Input, When Checked: Don't change the values programmed intothe unit.

grammar checker Message: This sentence is difficult to parse.

Please check for one of the following problems:

Then the grammar checker 620 goes on to list the typical and mostfrequent situations where parsing is made difficult if not impossiblethrough the use of grammatical constructions not included in therepertoire of CSL.

The punctuation usage in the sentence does not conform to CSLrestrictions. As noted above, punctuation marks and special characterswhich are not part of CSL in any context will be flagged by theVocabulary Checker 610. However, the Vocabulary Checker 610 does notparse input, so it will not report cases in which such an element existsin CSL but has been used in the wrong context. This kind of case willtrigger a "fail" response from the Grammar Checker 620.

A CSL vocabulary word was used in a syntactic form that is notrecognized for that word in CSL. The Vocabulary Checker 610 will flagsome of these cases; for example, if the word test is included in CSL asa noun but not as a verb, the Vocabulary Checker will report that thepast form tested is not CSL. However, the Vocabulary Checker 610 willallow the present verb form tests to pass, since that form is identicalto the plural CSL noun tests. This case will trigger a "fail" responsefrom the Grammar Checker 620.

The Grammar Checker 620 uses the MT Analysis module 127 (and the domainmodel 137) to identify sentences that do not conform to CSL grammaticalconstraints, this is known as syntactical analysis and is shown in block805. For each such sentence, the Grammar Checker 620 reports that thesentence is not CSL. It is also possible for a sentence to be in CSL butbe ambiguous. Consequently, the present invention provides semanticanalysis as shown in block 710. If the sentence being checked is notsemantically ambiguous, the disambiguation checker 630 will present someindication of the two or more possible meanings to the author andrequest clarification, as shown in blocks 815 and 825. In a preferredembodiment, when a sentence fails the Grammar Checker 620 and/or thedisambiguation checker 630, the author has the following options: editthe document, in cases of an ambiguous reading, disambiguate thesentence, recheck the same input, or continue checking without editing.

Note that the present invention implements absolute adherence toconstraints of vocabulary and grammar, rather than just stylisticwarnings or simple error detection (such as subject-verb agreement).

If the sentence is semantically unambiguous, then it is translated intoInterlingua, as shown in block 720. Once the document passes the grammarchecker 620, a SGML tag designating CSL approval can be inserted in thedocument.

In a preferred embodiment, the Grammar Checker 620 provides pass/failfeedback to the author 160. However, more specific feedback other thanpass/fail feedback can be implemented.

For a more in depth discussion of grammar checking, includingdisambiguation, see Tomita, M., "Sentence Disambiguation by Asking,"Computers and Translation, 1:39-51 (1986) and Carbonell, J. and M.Tomita, "Knowledge-Based Machine Translation, the CMU Approach," in S.Nirenburg (ed.), Machine Translation: Theoretical and MethodologicalIssues, Cambridge: Cambridge University Press, pgs. 68-89 (1987) both ofwhich are incorporated by reference.

F. Machine Translation

The MT 120 is an interlingua-type machine translation system. In suchsystems, the constrained source language (CSL) and the target languagenever come in direct contact. The processing in such systems generallyoccurs in two stages. First, representing the meaning of the CSL text ina language-independent formal language, called interlingua, and second,expressing this meaning using the lexical units and syntacticconstructions of the target language.

Interlingua MT systems, as well as other types of MT systems are wellknown in the art. Detailed descriptions of these different approaches tomachine translation can be found in Hutchins, Machine Translation: Past.Present. Future, Ellis Horwood, Ltd., Chichester, UK, 1986, andZarechnak, The History of Machine Translation, in Henisz-Dostert,McDonald, Zarechnak, eds., Machine Translation. Trends in Linguistics:Studies and Monographs, Vol. 11, The Hague, Mouton, 1979, both of whichare herein incorporated by reference in their entirety.

The meaning of the CSL text 350 is represented in the specially designedknowledge representation scheme called interlingua (which is well knownin the art). Interlingua is in turn represented in a frame notation andthus can be viewed as a kind of semantic network. Like other artificialor formal languages, interlingua has its own lexicon and syntax. Thelexicon is based on the domain from which the translated texts are taken(e.g., computer maintenance, space exploration, etc.). Thus, interlingua"nouns" are "object concepts" in the ontology; interlingua verbscorrespond, roughly, to "events" in the ontology; and interlinguaadjectives and adverbs are the various "properties" defined in theontology. The ontology forms a densely connected network for the varioustypes of concepts, called the domain model.

Referring to FIG. 3 and FIG. 9, the Machine Translation (MT) component120 of the IATS 105 contains two main sections. The first, the CSLanalyzer 127, performs the first processing stage of representing CSLtext in interlingua. The second main section, the Target LanguageGenerator 123, translates the interlingua representation of the"CSL-approved" texts into a target language (e.g., French, Japanese,Spanish). In performing both tasks, the MT component 120 runs as one ormore independent server modules, accepting translation requests from ahuman translation controller (not shown). During target languagegeneration, target language generator 123 maps the Interlingua text 260into the appropriate units of target language syntax to producehigh-quality output text 950 that requires no postediting.

Once the MT analysis module 127 has produced Interlingua text 260 for acertified CSL-compliant IE, that interlingua may be stored away,delivered, or converted immediately into a target language IE, or intoan IE in each of several target languages by the generator 123 (whichincludes a semantics-to-syntax mapper and a Generation Kit (Tomita M.and E. Nyberg, The Generation Kit and Transformation Version 3.2 User'sManual, Technical Memo (1988), available from the Center for MachineTranslation, Carnegie Mellon University, Pittsburgh, Pa.) sentencegenerator proper). MT analyzer 127 and MT generator 123 interact in twoways. First the output of the former is the input to the latter, andsecond they share some external knowledge sources, especially the domainmodel 137.

The MT system 120 is subdivided, as shown in FIG. 9. Analysis consistsof a Parser 910 and an Interpreter 920. The other half of the MT 120 canbe divided into a Mapper 930 and a Generator 940. The oval circles inFIG. 9 stand for the data that is produced and passed between the majorsoftware modules.

The DM 137 (and specifically the MT/DM 520) is used in three differentways during translation: (1) the parser 910 uses the DM 137 to constrainpossible attachments (using strict subcategorization of arguments andmodifiers during syntactic parsing); (2) the interpreter 920 uses the DM137 to instantiate the appropriate domain concepts duringinterpretation; (3) the mapper 930 uses the DM 137 to select theappropriate target realization for each interlingua concept.

The MT 120 runs as one or more server processes. Each such MT processaccepts translation requests from the FMS 110 and returns the results.The requests contain SGML-tagged CSL text and the results containSGML-tagged target language translations. Since translations into morethan one language may be going on at once, the requests also includedesired target language. Since the MT server processes are specializedby target language, a routing function is involved. This routingfunction is performed automatically by the FMS 110. The precise set ofMT processes running at a given time and their distribution acrossmachines is determined by the FMS 110, which will modify the mixaccording to the set of translation jobs outstanding at any particulartime.

Referring to FIG. 9, the CSL Analyzer 127 consists of two interconnectedcomponents--a syntactic parser 910 and a semantic interpreter 920.Semantic interpreter 920 is also known in the art as a "mapping ruleinterpreter." The syntactic parser 910 obtains the CSL text 305 inputand produces a syntactic structure for it. The syntactic parser 910 usesan LFG-type grammar. Lexical Functional Grammar (LFG) is a formalizedgrammar which is well known in the art of machine translation. As aresult, the resultant syntactic structure is an LFG f-structure 960. Assoon as the f-structure for the CSL sentence 960 is created, thesemantic interpreter 920 starts applying mapping rules in order tosubstitute source language lexical units and syntactic constructionswith their interlingua translations. Lexical units map into instances ofdomain concepts (e.g., the word "data" will map into the interlingua"information"), while syntactic structures map into conceptual relations(e.g., subjects of sentences often map into the "agent" relations ininterlingua). See Mitamura, The Hierarchical Organization of PredicateFrames for Interpretive Mapping in Natural Language Processing, Centerfor Machine Translation, Carnegie Mellon University (May 1990) which isincorporated by reference.

The MT analyzer 127, guided by analysis knowledge (data files),translates a CSL text 305 input sentence in the source language into asemantic frame representation of the meaning of the sentence. Theknowledge structures brought to bear in the analysis phase are theanalysis grammars, the mapping rules, and the concept lexicon.

The first part of the analysis is the parsing process, driven by thesyntactic analysis of the input sentence. The parser 910 uses thesemantic restrictions embodied in the concept lexicon (domain model) toguide its treatment of syntactic ambiguities encountered in its analysisof the input. The mapping rules mediate between the syntactic analysisgrammars and the concept lexicon.

The output of this analysis is syntactic f-structures containing allapplicable semantic information. This structure can be further processedby the second part of the MT analyzer 127 to produce asemantically-organized frame representation, in the form of theinstantiation of the relevant concepts from the concept lexicon thatwere encountered in parsing the sentence. The MT analyzer 127 arrives atthis form by retrieving the f-structure's semantic features; thesefeatures contain all relevant semantic information.

The syntactic parser 910 used in the present invention is well known inthe art and is described in detail in Tomita and Carbonell, TheUniversal Parser Architecture for Knowledge-Based Machine Translation,Technical Report, Center for Machine Translation, Carnegie MellonUniversity (May 1987) Tomita (ed.) et al., The Generalized LRParser/Compiler Version 8.1: User's Guide, Technical Memo, Center forMachine Translation, Carnegie Mellon University (April 1988) which areincorporated by reference.

One of the advantages of interlingua translation systems over othertypes of MT systems is that the interlingua 260 is language independent;that is, the subject and target languages are never in direct contact.This allows the construction of a machine translation system in whichpotentially any source and target languages could be selected whilerequiring minimal modifications to the computational structure. Clearly,then, any such system will need to be able to parse numerous sourcelanguages. Hence, a universal parser is needed which will take alanguage grammar as input, rather than build the grammar into theinterpreter proper. This allows greater extensibility and generality.

In other words, when dealing with multiple languages the linguisticstructure is no longer a universal invariant that transfers across allapplications (as it was for pure English language parsers), but ratheris another dimension of parameterization and extensibility. However,semantic information can remain invariant across languages (though, ofcourse, not across domains). Therefore, it is crucial to keep semanticknowledge sources separate from syntactic ones, so that if newlinguistic information is added it will apply across all semanticdomains, and if new semantic information is added it will apply to allrelevant languages. The universal parser attempts to accomplish thisfactoring without making major concessions to either run-time efficiencyor semantic accuracy.

The parser 910 is characterized by three kinds of knowledge sources. Onecontains syntactic grammars for different languages, another containssemantic knowledge bases for different domains, and the third containssets of rules which map syntactic forms (words and phases) into thesemantic knowledge structure. Each of the syntactic grammars iscompletely independent of any specific domain; likewise, each of thesemantic knowledge basis is independent of any specific domain;likewise, each of the semantic knowledge bases is independent of anyspecific language.

Further, the mapping rules are both language- and domain-dependent, anda different set of mapping rules is created for each language/domaincombination. Syntactic grammars, domain knowledge bases, and mappingrules are written in a highly abstract, human-readable manner. Thisorganization makes them easy to extend or modify, but possiblymachine-inefficient for a run-time parser.

The function of the mapping rule interpreter 920 is to generate andmanipulate the syntactic and semantic structures of a parse and,moreover, to generate these structures simultaneously.

The universal parser 910 produces all the possible, that is, valid,f-structures that can be derived from the sentences parsed. Each ofthese syntactic f-structures has semantic features, in accordance withLFG-theory these features are created at the same time as the rest ofthe syntactic f-structure. The semantic component may thus be regardedas an additional feature of f-structures.

Thus the semantic component is a "visible" part of the syntactic parse.The approach, of simultaneously creating the syntactic and semanticstructures, has produced a system able to eliminate "meaningless"partial parses before completing them. Semantics are added to thesyntactic structure when the lexicon is accessed for the definition of aword. Another part of the definition of a word is a set of structuralmapping rules. These mapping rules are used when syntactic equations ingrammar rules add infirmation to a syntactic structure.

The text language generator component 123 takes interlingua text 260 asits input and produces a target language text 950 as its output. Thetarget language generator 123 consist of two major modules, one semanticand one syntactic. The semantic performs the function of target languagelexical selection and choice of target language syntactic constructions;it is aided in these tasks by the generation lexicon and the generationstructure mapping rules, respectively. The output of this module is anf-structure of the target language sentence that will be output by thesystem.

The goal of the generation module is to produce target languagesentences from the interlingua text 260 frames produced by the CSLanalyzer 127. There are three main steps in generation:

1. Lexical Selection.

For each concept in the interlingua, the most appropriate lexical itemmust be selected.

2. F-Structure Creation.

A syntactic functional structure which determines the grammaticalstructure of the target utterance must be produced from the ILT frames.

3. Syntactic Generation.

The syntactic functional structure is processed by the generationgrammar to produce a target language sentence.

The design of the generation module 940 combines recent research in thearea of lexical selection with a map-and-generate paradigm that has beenutilized in previous translation systems.

For a more in depth discussion of machine translation and the specificdesign and operation of the modules described above see Nirenburg etal., Machine Translation: A Knowledge-Based Approach, Morgan KaufmannPublishers, Inc. (1992), Sommers & Hutchins, Introduction to MachineTranslation, Academic Press, London (October 1991), Mitamura et al., AnEfficient Interlingua Translation System for Multi-lingual DocumentProduction, Proceedings of Machine Translation Summit III, WashingtonD.C. (Jul. 2-4, 1991), Nirenburg, S., "World Knowledge and TextMeaning", in K. Goodman and S. Nirenburg (eds.), The KBMT Project: ACase Study in Knowledge-Based Machine Translation, San Mateo, Calif.:Morgan Kaufmann, KBMT-89 Project Report available from the Center forMachine Translation, Carnegie 5 Mellon University, Pittsburgh, Pa.(phone number (412) 268-6591) (4th Printing: March 1990), S. Nirenburg(ed.), Machine Translation: Theoretical and Methodological Issues,Cambridge: Cambridge University Press, pgs. 68-89 (1987), and Carbonellet al., Steps Toward Knowledge-Based Machine Translation, IEEETransaction on Pattern Analysis and Machine Intelligence, Vol. PAMI-3,No. 4 (July 1981) which are all hereby incorporated by reference.

While the invention has been particularly shown and described withreference to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A computer-based system for monolingual documentdevelopment, comprising:a text editor adapted to accept interactivelyfrom an author input text written in a source language; a languageeditor, which is an extension of said text editor, which interactivelyenforces lexical constraints and grammatical constraints on a naturallanguage subset used by said author to create said input text, whereinsaid author is interactively aided in enforcing said lexical constraintsand said grammmatical constraints on said input text so as to produceunambiguous constrained text; a machine translation system, responsiveto said language editor that is configured to translate said unambiguousconstrained text into a foreign language; and a domain model, whichcommunicates with said language editor, wherein said domain modelprovides pre-determined domain knowledge and linguistic semanticknowledge about lexical units and of their combinations, so as to assistsaid language editor in said enforcement of said lexical and grammaticalconstraints wherein said domain model is a tripartite domain model, saidtripartite domain model comprising,a kernel which contains lexicalinformation that is required by said language editor and said machinetranslation system, wherein said lexical information includes lexicalitems within said natural language subset along with associated semanticconcepts, parts of speech, and morphological information, a languageeditor domain model which contains information that is required only bysaid language editor, wherein said information includes at least one ofa natural language subset of synonyms for items not within said naturallanguage subset, a dictionary of definitions of said lexical items, anda set of examples of using said lexical items, and a machine translationdomain model which contains information which is required by only saidmachine translation system, said machine translation domain modelincludes a hierarchy of concepts used for unambiguous mapping andsemantic verification in translation.
 2. A computer-based system formonolingual document development, comprising:a text editor adapted toaccept interactively from a author information elements written in asource language; a language editor, which is an extension of said texteditor, which interactively enforces lexical and grammatical constraintson a natural language subset used by said author to create unambiguousconstrained information elements, wherein said author interactively aidsin enforcing said lexical and grammatical constraints on said input textso as to produce said unambiguous constrained information elements;memory means for storing said unambiguous constrained informationelements for subsequent use; a machine translation system, responsive tosaid language editor, that is configured to translate said unambiguousconstrained information elements into a foreign language; and a domainmodel, which communicates with said language editor, wherein said domainmodel provides pre-determined domain knowledge and linguistic semanticknowledge about lexical units and of their combinations, so as to assistsaid language editor in said enforcement of said lexical and grammaticalconstraints wherein said domain model is a tripartite domain model, saidtripartite domain model comprising,a kernel which contains lexicalinformation that is required by said language editor and said machinetranslation system, wherein said lexical information includes lexicalitems within said natural language subset along with associated semanticconcepts, parts of speech, and morphological information, a languageeditor domain model which contains information that is required only bysaid language editor, wherein said information includes at least one ofa natural language subset of synonyms for items not within said naturallanguage subset, a dictionary of definitions of said lexical items, anda set of examples of using said lexical items, and a machine translationdomain model which contains information which is required by only saidmachine translation system, said machine translation domain modelincludes a hierarchy of concepts used for unambiguous mapping andsemantic verification in translation.
 3. A computer-based method formonolingual document development, comprising the steps of:(1) enteringinput text in a source language into a text editor (2)checking, via alanguage editor, said input text against a pre-determined set ofconstraints stored in a domain model that provides pre-determined domainknowledge and linguistic semantic knowledge about lexical units and oftheir combinations, said pre-determined set of constraints includes aset of source sublanguage rules concerning vocabulary and grammar,wherein said domain model is a tripartite domain model, said tripartitedomain model comprising,a kernel which contains lexical information thatis required by said language editor and a machine translation system,wherein said lexical information includes lexical items that satisfysaid pre-determined set of constraints along with associated semanticconcepts, parts of speech, and morphological information, a languageeditor domain model which contains information that is required only bysaid language editor, wherein said information includes at least one ofa subset of synonyms for items that do not satisfy said pre-determinedset of constraints, a dictionary definitions of said lexical items, anda set of examples of using said lexical items, and a machine translationdomain model which contains information which is required by only saidmachine translation system, said machine translation domain modelincludes a hierarchy of concepts used for unambiguous mapping andsemantic verification in translation; (3) providing to an authorinteractive feedback relating to said input text, said interactivefeedback indicating if said pre-determined set of constraints is met,said interactive feedback is performed subsequent to referring to saiddomain model which provides the necessary domain knowledge andlinguistic semantic knowledge about lexical units and of theircombinations, and grammar of a subset of a natural language; and (4)producing, after completion of step (3), unambiguous constrained text.4. A computer-based method of claim 3, wherein said pre-determined setof constraints includes a set of source sublanguage rules concerningvocabulary and grammar, wherein said interactive feedback is performedin order to make said input text conform with said set of sourcesublanguage rules and to eliminate ambiguities.
 5. A computer-basedmethod for monolingual document development, comprising the steps of:(1)entering input text in a source language into a text editor; (2)checking, via a language editor, said input text against a constrainedsource language; (3) providing to an author interactive feedbackrelating to said source input text if non-constrained source language ispresent in said source input text until said author modifies said sourceinput text into a constrained source text, said interactive feedback isperformed after consulting a domain model which provides the necessarydomain knowledge and linguistic semantic knowledge about lexical unitsand of their combinations, wherein said domain model is a tripartitedomain model, said tripartite domain model comprising,a kernel whichcontains lexical information that is required by said language editorand said a machine translation system, wherein said lexical informationincludes lexical items within said constrained source language alongwith associated semantic concepts, parts of speech, and morphologicalinformation, a language editor domain model which contains informationthat is required only by said language editor, wherein said informationincludes at least one of a natural language subset of synonyms for itemsnot within said constrained source language, a dictionary definitions ofsaid lexical items, and a set of examples of using said lexical items,and a machine translation domain model which contains information whichis required by only said machine translation system, said machinetranslation domain model includes a hierarchy of concepts used forunambiguous mapping and semantic verification in translation; (4)checking for syntactic grammatical errors and semantic ambiguities insaid constrained source text by consulting said domain model; and (5)providing to said author interactive feedback to remove said syntacticgrammatical errors and said semantic ambiguities in said constrainedsource text to produce unambiguous constrained text.
 6. A computer-basedmethod for monolingual document development, comprising the steps of:(1)entering into a text editor at least one information element created ina source language; (2) checking, via a language editor, said at leastone information element against a constrained source language; (3)providing to an author interactive feedback relating to said at leastone information element if non-constrained source language is present insaid at least one information element until said at least oneinformation element has been modified into a constrained source text,said interactive feedback is performed after referring to a domain modelwhich provides the necessary domain knowledge and linguistic semanticknowledge about lexical units and their combinations, wherein saiddomain model is a tripartite domain model, said tripartite domain modelcomprising:a kernel which contains lexical information that is requiredby said language editor and said a machine translation system, whereinsaid lexical information includes lexical items within said constrainedsource language along with associated semantic concepts, parts ofspeech, and morphological information, a language editor domain modelwhich contains information that is required only by said languageeditor, wherein said information includes at least one of a naturallanguage subset synonyms for items not within said constrained sourcelanguage, a dictionary of definitions of said lexical items, and a setof examples of using said lexical items, and a machine translationdomain model which contains information which is required by only saidmachine translation system, said machine translation domain modelincludes a hierarchy of concepts used for unambiguous mapping andsemantic verification in translation; (4) checking for syntacticgrammatical errors and semantic ambiguities in said constrained sourcetext by consulting said domain model; (5) providing interactive feedbackto said author to remove said syntactic grammatical errors and saidsemantic ambiguities in said constrained source text to produce at leastone unambiguous constrained information element; and (6) saving said atleast one unambiguous constrained information element for later use. 7.A computer-based system for translating source language input text to aforeign language, comprising:a text editor adapted to acceptinteractively from an author the input text written in a sourcelanguage; a language editor, which is an extension of said text editor,which interacts with said author to produce from said input text anunambiguous constrained source text by interactively enforcingvocabulary and grammatical constraints against a constrained sourcelanguage; a machine translation system, responsive to said languageeditor, which is configured to translate said unambiguous constrainedsource text into the foreign language; and a domain model, whichcommunicates with said language editor and said machine translationsystem, and which provides predetermined domain knowledge and linguisticsemantic knowledge about lexical units and of their combinations, so asto aid in producing said unambiguous constrained source text and in saidtranslation to the foreign language, wherein said domain model is atripartite domain model, said tripartite domain model comprising,akernel which contains lexical informationthat is required by saidlanguage editor and said machine translation system, wherein saidlexical information includes lexical items within said constrainedsource language along with associated semantic concepts, parts ofspeech, and morphological information, a language editor domain modelwhich contains information that is required only by said languageeditor, wherein said information includes at least one of a subset ofsynonyms for items not within said constrained source language, adictionary definitions of said lexical items, and a set of examples ofusing said lexical items, and a machine translation domain model whichcontains information which is required by only said machine translationsystem, said machine translation domain model includes a hierarchy ofconcepts used for unambiguous mapping and semantic verification intranslation.
 8. The system of claim 7, further comprising means formarking with a tag a portion of said input text which has been renderedunambiguous constrained text by said interactive enforcement, whereinsaid tag indicates translatability.
 9. The system of claim 7, whereinsaid machine translation system operates in a translation serverenvironment which allows multiple authors to use the system.
 10. Thesystem of claim 7, wherein said author operates on a workstation whichis part of a computer network.
 11. The system of claim 7, wherein saidmachine translation system includes an interpreter which is configuredto translate said unambiguous constrained source text into interlingua.12. The system of claim 7, wherein said language editor provides saidinteraction with said author in a batch mode.
 13. The system of claim 7,further comprising a graphics editor adapted to create text labels,wherein said text labels can be edited by said author with the aid ofsaid language editor and subsequently translated by said machinetranslation system.
 14. The system of claim 7, wherein said constrainedsource language is a subset of a natural language, said constrainedsource language is specified as to lexicon and grammar.
 15. The systemof claim 7, wherein said language editor comprises a vocabulary checkerand a grammar checker.
 16. The system of claim 15, wherein saidvocabulary checker checks said input text against a permitted lexiconand suggests alternatives to non-lexicon word choices.
 17. The system ofclaim 15, wherein said grammar checker checks for compliance withpredefined grammatical rules and suggests alternatives to undefinedgrammatical structures.
 18. The system of claim 15, wherein said grammarchecker provides feedback to the author concerning lexical ambiguitiesand structural ambiguities.
 19. The system of claim 15, wherein saidgrammar checker provides a means for interactive disambiguation.
 20. Thesystem of claim 15, wherein said vocabulary checker includes a spellchecker.
 21. The system of claim 15, wherein said vocabulary checker isconfigured to identify words not included in said constrained sourcelanguage.
 22. The system of claim 7, wherein said input text is providedin blocks of information elements.
 23. The system of claim 22, whereinsaid information elements contain tags which enable said informationelements to be described in terms of their content and logicalstructure.
 24. A computer-based system for monolingual documentdevelopment and multilingual translation, comprising:a text editoradapted for accepting interactively from an author information elementswritten in a source language; a language editor, which is an extensionof said text editor, which interactively enforces lexical andgrammatical constraints on a natural language subset used by said authorto create said input text, wherein said author is interactively aided inenforcing said lexical and grammatical constraints on said informationelements to produce said unambiguous constrained information elements;machine translation system, responsive to said language editor, whichtranslates said unambiguous constrained information elements into aforeign language; and a domain model, which communicates with saidlanguage editor and said machine translation means, wherein said domainmodel provides pre-determined domain knowledge and linguistic semanticknowledge about lexical units and their combinations, so as to aid inproducing said unambiguous constrained source text and in saidtranslation to said foreign language, wherein said domain model is atripartite domain model, said tripartite domain model comprising,akernel which contains lexical information that is required by saidlanguage editor and said a machine translation system, wherein saidlexical information includes lexical items within said natural languagesubset along with associated semantic concepts, parts of speech, andmorphological information, a language editor domain model which containsinformation that is required only by said language editor, wherein saidinformation includes at least one of a natural language subset ofsynonyms for items not within said natural language subset, a dictionarydefinitions of said lexical items, and a set of examples of using saidlexical items, and a machine translation domain model which containsinformation which is required by only said machine translation system,said machine translation domain model includes a hierarchy of conceptsused for unambiguous mapping and semantic verification in translation.25. A computer-based system for monolingual document development andmultilingual translation, comprising:(A) a text editor adapted to acceptinteractively from an author input text written in a source language;(B) a language editor, which is an extension of said text editor, whichinteractively enforces lexical and grammatical constraints on a naturallanguage subset used by said author to create said input text, saidlanguage editor comprising,(i) a vocabulary checker which identifiesoccurrences of words that do not conform to said lexical constraints andwhich interactively aids said author in finding valid lexicalreplacements for said words that do not conform, and (ii) a grammarchecker which provides interactive feedback to said author concerningsyntactic and semantic ambiguity, said interactive feedback producingunambiguous constrained text; and (C) a domain model which communicateswith said language editor, wherein said domain model providespre-determined domain knowledge and linguistic semantic knowledge aboutlexical units and their combinations; and (D) a machine translationsystem, responsive to said language editor, which is configured totranslate said unambiguous constrained text into a foreign language,;wherein said domain model is a tripartite domain model, said tripartitedomain model comprising,a kernel which contains lexical information thatis required by said language editor and said a machine translationsystem, wherein said lexical information includes lexical items withinsaid natural language subset along with associated semantic concepts,parts of speech, and morphological information, a language editor domainmodel which contains information that is required only by said languageeditor, wherein said information includes at least one of a naturallanguage subset of synonyms for items not within said natural languagesubset, a dictionary definitions of said lexical items, and a set ofexamples of using said lexical items, and a machine translation domainmodel which contains information which is required by only said machinetranslation system, said machine translation domain model includes ahierarchy of concepts used for unambiguous mapping and semanticverification in translation.
 26. A computer-based method for translatingsource language text to a foreign language, comprising the steps of:(1)entering input text in a source language into a text editor; (2)checking, via a language editor, said input text against a constrainedsource language; (3) providing to an author interactive feedbackrelating to said source input text if nonconstrained source language ispresent in said source input text until said author modifies said sourceinput text into a constrained source text, said interactive feedbackincludes allowing said author to select, from a list of at least onesynonym, a word or phrase to replace said nonconstrained sourcelanguage; (4) checking for syntactic grammatical errors and semanticambiguities in said constrained source text; (5) providing interactivefeedback to said author to remove said syntactic grammatical errors andsaid semantic ambiguities in said constrained source text to produceunambiguous constrained source text; and (6) translating, via a machinetranslation system, said unambiguous constrained source text into atarget language; wherein steps (2) and (4) further include the step ofcommunicating with a tripartite domain model (DM), wherein saidtripartite DM provides predetermined domain knowledge and linguisticsemantic knowledge about lexical units and their combinations, saidtripartite domain model including,a kernel which contains lexicalinformation that is required by said language editor and said a machinetranslation system, wherein said lexical information includes lexicalitems within said constrained source language along with associatedsemantic concepts, parts of speech, and morphological information, alanguage editor domain model which contains information that is requiredonly by said language editor, wherein said information includes at leastone of a set of synonyms for items not within said constrained sourcelanguage, a dictionary of definitions of said lexical items, and a setof examples of using said lexical items, and a machine translationdomain model which contains information which is required by only saidmachine translation system, said machine translation domain modelincludes a hierarchy of concepts used for unambiguous mapping andsemantic verification in translation.
 27. The system of claim 26,further comprising the step of marking with a tag a portion of saidinput text which has been rendered unambiguous constrained source text,wherein said tag indicates translatability.
 28. The method of claim 26,wherein said step of translating first includes the step of translatingsaid constrained unambiguous text into interlingua.
 29. The method ofclaim 26, wherein said step (2) of check ing comprises the steps of:(a)checking a term from said source input text against a constrained sourcelanguage (CSL) lexical knowledgebase; (b) if the term is not found insaid CSL lexical knowledgebase then,(i) spellchecking said term againsta standard dictionary and allowing said author to correct the spellingof said term if it is misspelled; (ii) checking said term against saidCSL lexical database; and (iii) providing, if available, at least oneCSL synonym from said domain model if said term is not in said CSLlexical knowledgebase, and allowing said author to choose one of said atleast one synonym.
 30. The method of claim 29, further comprising thestep of repeating steps (a) and (b) for every term in said source inputtext.
 31. The method of claim 29, further comprising the step ofproviding a list of related CSL words or phrases to said author if saidterm has no direct CSL synonyms.
 32. The method of claim 29, furthercomprising the step of allowing said author to rewrite a sentencecontaining a non-CSL term.
 33. The method of claim 26, furthercomprising the step of inserting a tag into said source input text aftersaid author responds to said request for clarification of ambiguity. 34.The method of claim 26, wherein said source input text is created inblocks of information elements.
 35. The method of claim 26, wherein saidsource input text is a text label in a graphic.
 36. The method of claim26, wherein step (3) comprises the step of presenting an indication ofthe two or more possible meanings of said source input text to saidauthor.
 37. A computer-based method for monolingual document developmentand multilingual translation, comprising the steps of:(1) entering inputtext in a source language into a text editor; (2) checking, via alanguage editor, said input text against a predetermined set ofconstraints stored in a domain model, wherein said predetermined set ofconstraints includes a set of source sublanguage rules concerningvocabulary and grammar, wherein said interactive feedback is performedin order to make said input text conform with said set of sourcesublanguage rules and to eliminate ambiguities, wherein said domainmodel is a tripartite domain model, said tripartite domain modelcomprising,a kernel which contains lexical information that is requiredby said language editor and said a machine translation system, whereinsaid lexical information includes lexical items that satisfy saidpre-determined set of constraints along with associated semanticconcepts, parts of speech, and morphological information, a languageeditor domain model which contains information that is required only bysaid language editor, wherein said information includes at least one ofa set of synonyms for items that do not satisfy said pre-determined setof constraints, a dictionary of definitions of said lexical items, and aset of examples of using said lexical items, and a machine translationdomain model which contains information which is required by only saidmachine translation system, said machine translation domain modelincludes a hierarchy of concepts used for unambiguous mapping andsemantic verification in translation; (3) providing to an authorinteractive feedback relating to said input text if said predeterminedset of criteria is not met, said interactive feedback is performedsubsequent to consulting said domain model which provides the necessarydomain knowledge and linguistic semantic knowledge about lexical unitsand their combinations, wherein said author produces, through saidinteractive feedback, unambiguous constrained source text; (4)translating said unambiguous constrained source text into a targetlanguage.
 38. The system of claim 37, further comprising the step ofmarking with a tag a portion of said input text which has been renderedunambiguous constrained text, wherein said tag indicatestranslatability.
 39. A computer-based method for monolingual documentdevelopment and multilingual translation, the computer-based methodcomprising the steps of:(1) entering input text in a source languageinto a text editor; (2) checking, via a language editor, said input textagainst a constrained source language; (3) providing to an authorinteractive feedback relating to said source input text ifnonconstrained source language is present in said source input textuntil said source input text has been modified into a constrained sourcetext, said interactive feedback being done subsequent to consulting adomain model which provides the necessary domain knowledge andlinguistic semantic knowledge about lexical units and theircombinations, wherein said domain model is a tripartite domain model,said tripartite domain model comprising,a kernel which contains lexicalinformation that is required by said language editor and said a machinetranslation system, wherein said lexical information includes lexicalitems within said constrained source language along with associatedsemantic concepts, parts of speech, and morphological information, alanguage editor domain model which contains information that is requiredonly by said language editor, wherein said information includes at leastone of a natural language subset of synonyms for items not within saidconstrained source language, a dictionary of definitions of said lexicalitems, and a set of examples of using said lexical items, and a machinetranslation domain model which contains information which is required byonly said machine translation system, said machine translation domainmodel includes a hierarchy of concepts used for unambiguous mapping andsemantic verification in translation; (4) checking for syntacticgrammatical errors and semantic ambiguities in said constrained sourcetext by consulting said domain model; (5) providing interactive feedbackto said author to remove said syntactic grammatical errors and saidsemantic ambiguities in said constrained source text to produce at leaseone unambiguous constrained source text; and (6) saving said at leastone unambiguous constrained information element for later use; (7)translating with said machine translation system said at least oneunambiguous constrained source text into a foreign language.
 40. Acomputer-based method for monolingual document development andmultilingual translation, comprising the steps of:(1) entering into atext editor at least one information element created in a sourcelanguage; (2) checking, via a language editor, said at least oneinformation element against a constrained source language; (3) providingto an author interactive feedback relating to said at least oneinformation element if nonconstrained source language is present in saidat least one information element until said at least one informationelement has been modified into a constrained source test, saidinteractive feedback is performed after consulting a domain model whichprovides the necessary domain knowledge and linguistic semanticknowledge about lexical units and of their combinations, wherein saiddomain model is a tripartite domain model, said tripartite domain modelcomprising,a kernel which contains lexical information that is requiredby said language editor and said a machine translation system, whereinsaid lexical information includes lexical items within said naturallanguage subset along with associated semantic concepts, parts ofspeech, and morphological information, a language editor domain modelwhich contains information that is required only by said languageeditor, wherein said information includes at least one of a naturallanguage subset of synonyms for items not within said natural languagesubset, a dictionary of definitions of said lexical items, and a set ofexamples of using said lexical items, and a machine translation domainmodel which contains information which is required by only said machinetranslation system, said machine translation domain model includes ahierarchy of concepts used for unambiguous mapping and semanticverification in translation; (4) checking for syntactic grammaticalerrors and semantic ambiguities in said constrained text by consultingsaid domain model; (5) providing interactive feedback to said author toremove said syntactic grammatical errors and said semantic ambiguitiesin said constrained source text to produce at least one unambiguousconstrained information element; (6) saving said at least oneunambiguous constrained information element for later use; (7)translating with said machine translation system said at least oneunambiguous constrained information element into a foreign language. 41.The method of claim 40, further comprising the step of marking with atag said information element certifying it to be translatable.
 42. Themethod of claim 40, wherein step (3) of providing interactive feedbackincludes the step of allowing said author to select from a list ofsynonyms a word or phrase to replace said nonconstrained language insaid at least one information element.